This document describes cuFFT, the NVIDIA® CUDA™ Fast Fourier Transform (FFT) product. It consists of two separate libraries: cuFFT and cuFFTW. The cuFFT library is designed to provide high performance on NVIDIA GPUs. The cuFFTW library is provided as a porting tool to enable users of FFTW to start using NVIDIA GPUs with a minimum amount of effort. The FFT is a divide-and-conquer algorithm for efficiently computing discrete Fourier transforms of complex or real-valued data sets. It is one of the most important and widely used numerical algorithms in computational physics and general signal processing. The cuFFT library provides a simple interface for computing FFTs on an NVIDIA GPU, which allows users to quickly leverage the floating-point power and parallelism of the GPU in a highly optimized and tested FFT library. The cuFFT product supports a wide range of FFT inputs and options efficiently on NVIDIA GPUs. This version of the cuFFT library supports the following features: ...

References in zbMATH (referenced in 17 articles )

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  1. Antti-Pekka Hynninen, Dmitry I. Lyakh: cuTT: A High-Performance Tensor Transpose Library for CUDA Compatible GPUs (2017) arXiv
  2. Peter Steinbach, Matthias Werner: gearshifft - The FFT Benchmark Suite for Heterogeneous Platforms (2017) arXiv
  3. Lončar, Vladimir; Balaž, Antun; Bogojević, Aleksandar; Škrbić, Srdjan; Muruganandam, Paulsamy; Adhikari, Sadhan K.: CUDA programs for solving the time-dependent dipolar Gross-Pitaevskii equation in an anisotropic trap (2016)
  4. Pilar Cossio, David Rohr, Fabio Baruffa, Markus Rampp, Volker Lindenstruth, Gerhard Hummer: BioEM: GPU-accelerated computing of Bayesian inference of electron microscopy images (2016) arXiv
  5. Einkemmer, Lukas; Ostermann, Alexander: On the error propagation of semi-Lagrange and Fourier methods for advection problems (2015)
  6. Feng, Chunsheng; Shu, Shi; Xu, Jinchao; Zhang, Chen-Song: Numerical study of geometric multigrid methods on CPU-GPU heterogeneous computers (2014)
  7. Leclaire, Sébastien; El-Hachem, Maud; Trépanier, Jean-Yves; Reggio, Marcelo: High order spatial generalization of 2D and 3D isotropic discrete gradient operators with fast evaluation on GPUs (2014)
  8. Yang, Yi; Zhou, Huiyang: A highly efficient FFT using shared-memory multiplexing (2014)
  9. Chen, Yifeng: Algebraic program semantics for supercomputing (2013)
  10. Gai, Jiading; Obeid, Nady; Holtrop, Joseph L.; Wu, Xiao-Long; Lam, Fan; Fu, Maojing; Haldar, Justin P.; Hwu, Wen-mei W.; Liang, Zhi-Pei; Sutton, Bradley P.: More IMPATIENT: a gridding-accelerated Toeplitz-based strategy for non-Cartesian high-resolution 3D MRI on gpus (2013) ioport
  11. Alcaraz-Pelegrina, J.M.; Rodríguez-García, P.: Simulations of pulse propagation in optical fibers using graphics processor units (2011)
  12. Maintz, Stefan; Eck, Bernhard; Dronskowski, Richard: Speeding up plane-wave electronic-structure calculations using graphics-processing units (2011)
  13. Rossinelli, Diego; Bergdorf, Michael; Cottet, Georges-Henri; Koumoutsakos, Petros: GPU accelerated simulations of bluff body flows using vortex particle methods (2010)
  14. Ruiz, Antonio; Ujaldon, Manuel; Cooper, Lee; Huang, Kun: Non-rigid registration for large sets of microscopic images on graphics processors (2009) ioport
  15. Che, Shuai; Boyer, Michael; Meng, Jiayuan; Tarjan, David; Sheaffer, Jeremy W.; Skadron, Kevin: A performance study of general-purpose applications on graphics processors using CUDA (2008) ioport
  16. Ruiz, Antonio; Ujaldon, Manuel; Cooper, Lee; Huang, Kun: Non-rigid registration for large sets of microscopic images on graphics processors (2008) ioport
  17. Stone, S.S.; Haldar, J.P.; Tsao, S.C.; Hwu, W.-M.W.; Sutton, B.P.; Liang, Z.-P.: Accelerating advanced MRI reconstructions on gpus (2008) ioport